library(ggplot2)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ✓ purrr   0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
download.file(url = "https://ndownloader.figshare.com/files/2292169",
              destfile = "data/portal_data_joined.csv")
surveys_complete <- read_csv("data/portal_data_joined.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   record_id = col_double(),
##   month = col_double(),
##   day = col_double(),
##   year = col_double(),
##   plot_id = col_double(),
##   species_id = col_character(),
##   sex = col_character(),
##   hindfoot_length = col_double(),
##   weight = col_double(),
##   genus = col_character(),
##   species = col_character(),
##   taxa = col_character(),
##   plot_type = col_character()
## )

Template for different plots

#ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
ggplot(data = surveys_complete)

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length))

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point()
## Warning: Removed 4048 rows containing missing values (geom_point).

# Assign plot to a variable
surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes(x = weight, y = hindfoot_length))

# Draw the plot
surveys_plot + 
    geom_point()
## Warning: Removed 4048 rows containing missing values (geom_point).

# Challenge 1 Scatter plots can be useful exploratory tools for small datasets. For data sets with large numbers of observations, such as the surveys_complete data set, overplotting of points can be a limitation of scatter plots. One strategy for handling such settings is to use hexagonal binning of observations. The plot space is tessellated into hexagons. Each hexagon is assigned a color based on the number of observations that fall within its boundaries. To use hexagonal binning with ggplot2, first install the R package hexbin from CRAN:

library(hexbin)

#Then use the geom_hex() function:

surveys_plot +
 geom_hex()
## Warning: Removed 4048 rows containing non-finite values (stat_binhex).

#What are the relative strengths and weaknesses of a hexagonal bin plot compared to a scatter plot? Examine the above scatter plot and compare it with the hexagonal bin plot that you created.

Challenge 3 Create a violin plot to show shape of distribution

ggplot(data= surveys_complete, mapping = aes(x = species_id, y = weight)) + 
  geom_violin()
## Warning: Removed 2503 rows containing non-finite values (stat_ydensity).

# In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot. Changing the scale of the axes is done similarly to adding/modifying other components (i.e., by incrementally adding commands). Try making these modifications:

ggplot(data= surveys_complete, mapping = aes(x = species_id, y = weight)) + scale_y_log10() +
  geom_violin()
## Warning: Removed 2503 rows containing non-finite values (stat_ydensity).

# So far, we’ve looked at the distribution of weight within species. Try making a new plot to explore the distribution of another variable within each species. Create boxplot for hindfoot_length. Overlay the boxplot layer on a jitter layer to show actual measurements. Add color to the data points on your boxplot according to the plot from which the sample was taken (plot_id).

ggplot(data= surveys_complete, mapping = aes(x = species_id, y = sex)) + 
  geom_violin(aes(color = sex))

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) + geom_violin(alpha = 0.1) + geom_jitter(aes(color = as_factor(species_id))) 
## Warning: Removed 3348 rows containing non-finite values (stat_ydensity).
## Warning: Removed 3348 rows containing missing values (geom_point).

# Line Graphs

yearly_counts <- surveys_complete %>%
  count(year, genus)
ggplot(data = yearly_counts, aes(x = year, y = n)) +
     geom_line()

ggplot(data = yearly_counts, aes(x = year, y = n, group = genus)) +
    geom_line()

ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts_graph <- surveys_complete %>%
    count(year, genus) %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts_graph

# Faceting = ggplot has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a time series plot for each species:

ggplot(data = yearly_counts, aes(x = year, y = n)) +
    geom_line() +
    facet_wrap(facets = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

yearly_sex_counts <- surveys_complete %>%
                      count(year, genus, sex)
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(facets =  vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(sex), cols =  vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

# One column, facet by rows
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

# One row, facet by column
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

#ggplot themes = Usually plots with white background look more readable when printed. Every single component of a ggplot graph can be customized using the generic theme() function, as we will see below. However, there are pre-loaded themes available that change the overall appearance of the graph without much effort.

ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_bw()
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

# Challenge 4 Use what you just learned to create a plot that depicts how the average weight of each species changes through the years.

ggplot(data = surveys_complete, 
        mapping = aes(x = year, y = weight, color = species_id)) +
     geom_line() +
     facet_wrap(vars(species_id))
## Warning: Removed 1314 row(s) containing missing values (geom_path).

# Customization

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
         x = "Year of observation",
         y = "Number of individuals") +
    theme_bw()
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(text=element_text(size = 16))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12, 
                                               angle = 90, hjust = 0.5, 
                                               vjust = 0.5),
                    axis.text.y = element_text(colour = "grey20", size = 12),
                    text=element_text(size = 16))

ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot() +
    grey_theme
## Warning: Removed 3348 rows containing non-finite values (stat_boxplot).

# Challenge 5 With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration.

 ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line(size = 2) +
     facet_wrap(vars(genus)) +
     labs(title = "Yearly Sex Counts",
        x = "Year",
        y = "Number of Individuals") + scale_color_manual(values=c("Green","Blue")) 
## Warning: Removed 265 row(s) containing missing values (geom_path).
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

# Arranging Plots

library(patchwork)
plot1 <- ggplot(data = surveys_complete, aes(x = species_id, y = weight)) +
  geom_boxplot() +
  labs(x = "Species", y = expression(log[10](Weight))) +
  scale_y_log10()

plot2 <- ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
  geom_line() + 
  labs(x = "Year", y = "Abundance")

plot1 / plot2 + plot_layout(heights = c(3, 2))
## Warning: Removed 2503 rows containing non-finite values (stat_boxplot).

# Exporting Plots

my_plot <- ggplot(data = yearly_sex_counts, 
                  aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90,
                                     hjust = 0.5, vjust = 0.5),
          axis.text.y = element_text(colour = "grey20", size = 12),
          text = element_text(size = 16))

ggsave("name_of_file.png", my_plot, width = 15, height = 10)
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?